Outline of Figures:

Fig1: Drosophila egg chambers exhibit a characteristic network structure of connections between nurse cells A) Cartoon of tissue, B) Equivalent image of tissue, C) Diagram of connections between NCs via RCs. optional caption text optional caption text

Fig2: Behaviour of differential equation model. A) Dynamics in time shown for each cell, B) Model related to network structure of nurse cells.

## 
##  Elapsed Time: 0 seconds (Warm-up)
##                0.054887 seconds (Sampling)
##                0.054887 seconds (Total)
## 
## 
##  Elapsed Time: 0 seconds (Warm-up)
##                0.044855 seconds (Sampling)
##                0.044855 seconds (Total)
## 
## 
##  Elapsed Time: 0 seconds (Warm-up)
##                0.049195 seconds (Sampling)
##                0.049195 seconds (Total)
## 
## 
##  Elapsed Time: 0 seconds (Warm-up)
##                0.047305 seconds (Sampling)
##                0.047305 seconds (Total)
## 
## 
##  Elapsed Time: 0 seconds (Warm-up)
##                0.057892 seconds (Sampling)
##                0.057892 seconds (Total)
## 
## 
##  Elapsed Time: 0 seconds (Warm-up)
##                0.056756 seconds (Sampling)
##                0.056756 seconds (Total)
## 
## 
##  Elapsed Time: 0 seconds (Warm-up)
##                0.071297 seconds (Sampling)
##                0.071297 seconds (Total)

Fig3: Assembly of higher order complexes in oocyte A) Particles in different regions of the egg chamber, B) Distribution of intensities of particles in different regions.

## # A tibble: 3 x 5
##   phenotype    av   std av_median stnd_error
##   <chr>     <dbl> <dbl>     <dbl>      <dbl>
## 1 OE        0.508 0.608     0.300     0.0287
## 2 UE        1.62  1.63      1.03      0.122 
## 3 WT        0.563 0.702     0.345     0.0380

Fig4: Characterisation of the bias in directionality of transport through ring canals A) Schematic of transport between two compartments, B) Posterior pairs plot, C) Posterior predictive plot.

##    purple
## 1 #e5cce5
## 2 #bf7fbf
## 3 #a64ca6
## 4 #800080
## 5 #660066
## 6 #400040

Fig5: Results of inference for dynamic model A) Posterior pairs plot, B) Posterior predictive distribution, C) Sensitivity to a, b.

## [1] "using real data \n"
## Inference for Stan model: mrna_transport_no_decay.
## 4 chains, each with iter=2000; warmup=1000; thin=1; 
## post-warmup draws per chain=1000, total post-warmup draws=4000.
## 
##        mean se_mean   sd 2.5%   25%   50%   75% 97.5% n_eff Rhat
## a     13.24    0.06 2.57 9.48 11.45 12.90 14.56 18.92  1630    1
## b      0.24    0.00 0.05 0.16  0.21  0.24  0.27  0.35  1800    1
## nu     0.96    0.00 0.04 0.86  0.94  0.97  0.98  1.00  2695    1
## phi    0.33    0.00 0.04 0.26  0.30  0.33  0.35  0.39  2411    1
## sigma  1.21    0.00 0.13 0.98  1.12  1.20  1.30  1.48  2718    1
## 
## Samples were drawn using NUTS(diag_e) at Fri Sep 14 17:42:45 2018.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at 
## convergence, Rhat=1).

## [1] "using real data \n"
## [1] "swapped"
## [1]  0.00001  2.40820 12.43159 14.79800 19.54926 21.98536 22.69240 22.81189
## [9] 24.29904
## [1] 9
## Inference for Stan model: mrna_transport_no_decay.
## 4 chains, each with iter=2000; warmup=1000; thin=1; 
## post-warmup draws per chain=1000, total post-warmup draws=4000.
## 
##         mean se_mean   sd  2.5%    25%    50%    75%  97.5% n_eff Rhat
## a      32.32    0.10 6.48 20.45  27.84  32.17  36.44  45.77  4000    1
## b     109.12    0.11 7.01 95.56 104.32 109.06 113.84 122.95  4000    1
## nu      0.56    0.00 0.04  0.47   0.53   0.56   0.59   0.64  3033    1
## phi     0.57    0.00 0.03  0.51   0.55   0.57   0.59   0.63  4000    1
## sigma   0.02    0.00 0.00  0.02   0.02   0.02   0.03   0.03  4000    1
## 
## Samples were drawn using NUTS(diag_e) at Tue Sep 18 20:28:11 2018.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at 
## convergence, Rhat=1).

optional caption text

Fig7: Validation of testable prediction for over expression versus measured results.

FigS1: (Supplementary) Exponential growth model to establish timescales A) Schematic of time points used for each stage, B) Linear regression of log(A) against the time points for each stage.

## $t0
## # A tibble: 3 x 2
##   split          estimate
##   <fct>             <dbl>
## 1 Overexpression     6.90
## 2 test               7.86
## 3 train              7.57
## 
## $ts1
## [1] 14.62700 17.34324 17.97174 23.47762 23.55478 25.04558 25.92174 30.41032
## [9] 32.14798
## 
## $ts2
##  [1]  0.00001  2.40820 11.31019 12.43159 14.62700 14.79800 17.28339
##  [8] 17.34324 17.97174 19.54926 19.66385 20.77397 20.90405 21.60169
## [15] 21.78633 21.90111 21.98536 22.69240 22.81189 23.47762 23.55478
## [22] 24.29904 25.04558 25.26811 25.92174 26.88113 26.96548 30.41032
## [29] 32.14798
## 
## $ts3
##  [1]  0.00001  2.40820 11.31019 12.43159 14.79800 17.28339 19.54926
##  [8] 19.66385 20.77397 20.90405 21.60169 21.78633 21.90111 21.98536
## [15] 22.69240 22.81189 24.29904 25.26811 26.88113 26.96548
## 
## $ts4
## [1]  0.00001  2.40820 12.43159 14.79800 19.54926 21.98536 22.69240 22.81189
## [9] 24.29904
## 
## $sort_indices1
## [1] 2 4 8 3 7 9 1 5 6
## 
## $sort_indices2
##  [1] 28 22 10 24  2 26 17  4  8 27 14 15 12 18 11 20 25 21 23  3  7 29  9
## [24] 19  1 13 16  5  6
## 
## $sort_indices3
##  [1]  1  8  5  6  3  9  2 11 10  4  7
## 
## $sort_indices4
## [1] 8 2 4 6 7 5 1 3 9
## 
## $time_scaling
## # A tibble: 3 x 2
##   split          estimate
##   <fct>             <dbl>
## 1 Overexpression   0.0715
## 2 test             0.0425
## 3 train            0.0559

FigS2: Convergence diagnostics for MCMC inference methods

FigS3: Prior predictive distribution

## [1] "using real data \n"
## [1] "Using the following stan file: prior_predictive_no_decay.stan"
## Inference for Stan model: prior_predictive_no_decay.
## 4 chains, each with iter=2000; warmup=1000; thin=1; 
## post-warmup draws per chain=1000, total post-warmup draws=4000.
## 
##          mean se_mean   sd 2.5%  25%  50%   75% 97.5% n_eff Rhat
## theta[1] 7.88    0.09 5.98 0.29 3.15 6.59 11.32 22.32  4000    1
## theta[2] 8.03    0.10 6.15 0.27 3.09 6.75 11.66 22.95  4000    1
## theta[3] 0.50    0.00 0.29 0.02 0.25 0.50  0.74  0.97  4000    1
## phi      2.97    0.02 0.99 1.09 2.29 2.96  3.63  4.98  3664    1
## sigma    0.79    0.01 0.60 0.03 0.31 0.66  1.15  2.22  3998    1
## a        8.03    0.10 6.15 0.27 3.09 6.75 11.66 22.95  4000    1
## b        7.88    0.09 5.98 0.29 3.15 6.59 11.32 22.32  4000    1
## 
## Samples were drawn using NUTS(diag_e) at Thu Sep 27 16:45:34 2018.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at 
## convergence, Rhat=1).

FigSPairs: Posterior pairwise plot

FigS4: Sensitivity analysis